用脑成像和机器学习精准预测帕金森病运动症状严重程度。
Interpretable machine learning predicts Parkinson's disease severity using motion-corrected QSM MRI and multiband multiecho fMRI features
- 结合定量磁化率与多回波功能影像，用可解释模型预测病情。
- 模型解释度高，75%患者预测误差在±5分内，最优结果达45.4%方差解释率。
- 适合神经影像与临床研究者参考，尤其关注可解释性与多模态融合。
客观神经影像生物标志物可提升帕金森病运动评估的准确性，捕捉临床检查难以察觉的大脑变化。本研究利用可解释机器学习，基于定量磁化率（QSM）和多带多回波静息态功能磁共振成像（fMRI）提取的局部一致性（ReHo）特征，预测运动症状严重程度（以MDS-UPDRS Part III评分衡量）。对28名受试者（24例帕金森病患者，4名健康对照）进行分析，设计13组特征实验，涵盖影像、临床、多模态输入。采用支持向量回归、弹性网络、随机森林和XGBoost模型，通过嵌套交叉验证训练并评估性能，指标包括合并保留测试集的R²、RMSE、MAE、皮尔逊相关系数、置换检验及预测误差±5分内的比例。结果显示，仅影像模型具有显著预测能力，而仅临床模型表现较弱。全量fMRI、全量QSM和临床变量共同提供最强全局拟合，解释了45.4%的运动严重程度方差。最优组合为选中的QSM+临床变量，75.0%患者预测误差在±5分内，且平均绝对误差（MAE）最低。SHAP分析揭示小脑、丘脑、纹状体、岛叶及运动皮层特征贡献显著。结论表明，QSM与多回波fMRI-ReHo能捕捉帕金森病运动严重程度的不同可解释维度，结构与功能影像在不同临床目标下贡献各异。
原文摘要 · Abstract (English)
Introduction: Objective neuroimaging biomarkers may improve Parkinson's disease motor assessment by capturing brain variation not directly observable from clinical examination. We used interpretable machine learning to predict current motor severity, measured by MDS-UPDRS Part III, from QSM and multiband multi-echo resting-state fMRI-derived ReHo features. Methods: Regional QSM and ReHo features were extracted from 28 participants, including 24 individuals with Parkinson's disease and 4 controls. Thirteen feature-set experiments evaluated imaging-only, clinical-only, imaging-plus-clinical, full, reduced, and multimodal inputs. Support vector regression, Elastic Net, Random Forest, and XGBoost models were trained using nested cross-validation. Performance was assessed using pooled held-out R^2, RMSE, MAE, Pearson correlation, permutation testing, and the proportion of participants predicted within +/-5 MDS-UPDRS Part III points. Results: Imaging-only models carried meaningful predictive signal, whereas the clinical-only model performed weakly. Full fMRI, full QSM, and clinical variables provided the strongest global fit, explaining 45.4% of variance in motor severity. Selected QSM plus clinical variables produced the most clinically close predictions, with 75.0% of participants predicted within +/-5 points and the lowest MAE among top-performing models. SHAP highlighted cerebellar, thalamic, striatal, insular, and motor cortical features. Conclusion: QSM and multiband multi-echo fMRI-derived ReHo capture distinct, interpretable dimensions of Parkinson's disease motor severity. These findings show that structural and functional imaging contribute differently depending on the clinical prediction goal.
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